{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'D:\\\\Environment\\\\Anaconda3\\\\python.exe'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "sys.executable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.3.1'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看 keras 的版本号\n",
    "import keras\n",
    "keras.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5-2-1 挑选4000张图片，划分训练集，验证集和测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, shutil\n",
    "\n",
    "# 解压数据集后，存放 25000 张训练图像的文件夹，图片命名为 cat.xxx.jpg, dog.xxx.jpg，xxx是编号，从 0 到 12499\n",
    "original_dataset_dir = './train'\n",
    "\n",
    "base_dir = './cats_and_dogs_small' # 保存较小数据集的目录（4000张图像）\n",
    "os.mkdir(base_dir)\n",
    "\n",
    "# 分别创建训练集，验证集和测试集图像存放的文件夹\n",
    "train_dir = os.path.join(base_dir, 'train')  # 训练集\n",
    "os.mkdir(train_dir)\n",
    "validation_dir = os.path.join(base_dir, 'validation') # 验证集\n",
    "os.mkdir(validation_dir)\n",
    "test_dir = os.path.join(base_dir, 'test') # 测试集\n",
    "os.mkdir(test_dir)\n",
    "\n",
    "# 在上面3个文件夹中，每个文件夹中分别创建猫，狗的图像目录\n",
    "train_cats_dir = os.path.join(train_dir, 'cats')  # 猫的训练图像目录\n",
    "os.mkdir(train_cats_dir)\n",
    "train_dogs_dir = os.path.join(train_dir, 'dogs')  # 狗的训练图像目录\n",
    "os.mkdir(train_dogs_dir)\n",
    "validation_cats_dir = os.path.join(validation_dir, 'cats')  # 猫的验证图像目录\n",
    "os.mkdir(validation_cats_dir)\n",
    "validation_dogs_dir = os.path.join(validation_dir, 'dogs')  # 狗的验证图像目录\n",
    "os.mkdir(validation_dogs_dir)\n",
    "test_cats_dir = os.path.join(test_dir, 'cats')  # 猫的测试图像目录\n",
    "os.mkdir(test_cats_dir)\n",
    "test_dogs_dir = os.path.join(test_dir, 'dogs')  # 狗额测试图像目录\n",
    "os.mkdir(test_dogs_dir)\n",
    "\n",
    "# 从 25000 张图像中选择 4000 张图像，加到上述文件夹中\n",
    "fnames = ['cat.{}.jpg'.format(i) for i in range(1000)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(train_cats_dir, fname)\n",
    "    shutil.copyfile(src, dst) # 将前 1000 张猫的图像复制到 train_cats_dir\n",
    "\n",
    "fnames = ['cat.{}.jpg'.format(i) for i in range(1000, 1500)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(validation_cats_dir, fname)\n",
    "    shutil.copyfile(src, dst) # 将接下来 500 张猫的图像复制到 validation_cats_dir\n",
    "    \n",
    "fnames = ['cat.{}.jpg'.format(i) for i in range(1500, 2000)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(test_cats_dir, fname)\n",
    "    shutil.copyfile(src, dst) # 将接下来的 500 张猫的图像复制到 test_cats_dir\n",
    "\n",
    "fnames = ['dog.{}.jpg'.format(i) for i in range(1000)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(train_dogs_dir, fname)\n",
    "    shutil.copyfile(src, dst) # 将前 1000 张狗的图像复制到 train_dogs_dir\n",
    "    \n",
    "fnames = ['dog.{}.jpg'.format(i) for i in range(1000, 1500)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(validation_dogs_dir, fname)\n",
    "    shutil.copyfile(src, dst) # 将接下来 500 张狗的图像复制到 validation_dogs_dir\n",
    "\n",
    "fnames = ['dog.{}.jpg'.format(i) for i in range(1500, 2000)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(test_dogs_dir, fname)\n",
    "    shutil.copyfile(src, dst) # 将接下来 500 张狗的图像复制到 test_dogs_dir"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "运行之后的文件目录结构如下图所示：\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们来检查一下，看看每个分组（训练 / 验证 / 测试）中分别包含多少张图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "猫的训练集图像数量 1000\n",
      "狗的训练集图像数量 1000\n",
      "-----------------------------------\n",
      "猫的验证集图像数量 500\n",
      "狗的验证集图像数量 500\n",
      "-----------------------------------\n",
      "猫的测试集图像数量 500\n",
      "狗的测试集图像数量 500\n"
     ]
    }
   ],
   "source": [
    "print('猫的训练集图像数量', len(os.listdir(train_cats_dir)))\n",
    "print('狗的训练集图像数量', len(os.listdir(train_dogs_dir)))\n",
    "print('-----------------------------------')\n",
    "print('猫的验证集图像数量', len(os.listdir(validation_cats_dir)))\n",
    "print('狗的验证集图像数量', len(os.listdir(validation_dogs_dir)))\n",
    "print('-----------------------------------')\n",
    "print('猫的测试集图像数量', len(os.listdir(test_cats_dir)))\n",
    "print('狗的测试集图像数量', len(os.listdir(test_dogs_dir)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "所以我们的确有 2000 张训练图像、1000 张验证图像和 1000 张测试图像。\n",
    "每个分组中两个类别的样本数相同，这是一个平衡的二分类问题，分类精度可作为衡量成功的指标。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里我们的卷积神经网络由 **Conv2D 层（使用 relu 激活）**和**MaxPooling2D** 层交替堆叠构成。\n",
    "但是由于是处理更大的图像和更复杂的问题，我们要相应地增大网络，即增加一个 Conv2D + MaxPolling2D 的组合。\n",
    "这既可以增大网络容量，也可以进一步减小特征图的尺寸，使其在连接 Flatten 层时尺寸不会太大。\n",
    "本例中初始输入的尺寸为 150×150（有些随意的选择），所以最后在 Flatten 层之前的特征图大小为 7×7。\n",
    "\n",
    "网络中特征图的深度在逐渐增大（从 32 增大到 128），而特征图的尺寸在逐渐减小（从150×150 减小到 7×7）。这几乎是所有卷积神经网络的模式。\n",
    "\n",
    "我们面对的是一个二分类问题，所以网络最后一层是使用 sigmoid 激活的单一单元（大小为1 的 Dense 层）。这个单元将对某个类别的概率进行编码。\n",
    "\n",
    "在编译这一步，和前面一样，我们将使用 RMSprop 优化器。因为网络最后一层是单一 sigmoid 单元，所以我们将使用二元交叉熵作为损失函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5-2-2 实例化卷积神经网络\n",
    "\n",
    "#### 将猫狗分类的小型卷积神经网络实例化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import layers\n",
    "from keras import models\n",
    "\n",
    "# 使用 Sequential\n",
    "model = models.Sequential()\n",
    "\n",
    "# 第1个 Conv2D + MaxPooling2D 组合\n",
    "model.add(layers.Conv2D(32, (3, 3), activation='relu',input_shape=(150, 150, 3)))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "\n",
    "# 第2个 Conv2D + MaxPooling2D 组合\n",
    "model.add(layers.Conv2D(64, (3, 3), activation=\"relu\"))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "\n",
    "# 第3个 Conv2D + MaxPooling2D 组合\n",
    "model.add(layers.Conv2D(128, (3, 3), activation=\"relu\"))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "\n",
    "# 第4个 Conv2D + MaxPooling2D 组合\n",
    "model.add(layers.Conv2D(128, (3, 3), activation=\"relu\"))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(512, activation=\"relu\"))\n",
    "model.add(layers.Dense(1, activation=\"sigmoid\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面来看一下特征图的维度如何随着每层变化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_18 (Conv2D)           (None, 148, 148, 32)      896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_13 (MaxPooling (None, 74, 74, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_19 (Conv2D)           (None, 72, 72, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_14 (MaxPooling (None, 36, 36, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_20 (Conv2D)           (None, 34, 34, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_15 (MaxPooling (None, 17, 17, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_21 (Conv2D)           (None, 15, 15, 128)       147584    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_16 (MaxPooling (None, 7, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_4 (Flatten)          (None, 6272)              0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 512)               3211776   \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 1)                 513       \n",
      "=================================================================\n",
      "Total params: 3,453,121\n",
      "Trainable params: 3,453,121\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5-2-3 编译模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import optimizers\n",
    "\n",
    "# 编译模型\n",
    "model.compile(loss=\"binary_crossentropy\", optimizer=optimizers.RMSprop(lr=1e-4), metrics=['acc'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5-2-4 数据预处理\n",
    "将数据输入神经网络之前，应该将数据格式化为经过预处理的浮点数张量。\n",
    "现在，数据以 JPEG 文件的形式保存在硬盘中，所以数据预处理步骤大致如下:\n",
    "（1） 读取图像文件\n",
    "（2） 将 JPEG 文件编码为 RGB 像素网格\n",
    "（3） 将这些像素网格尊皇为浮点数张量\n",
    "（4） 将像素值（0~255范围内）缩放到 [0,1] 区间（神经网络喜欢处理较小的输入值）\n",
    "\n",
    "我们可以使用 Keras 的图像处理辅助工具模块，位 keras.preprocessing.image来帮助我们完成这些步骤，模块中的 ImageDataGenerator 类可以快速创建 Python 生成器，能够将硬盘上的图像文件自动转换\n",
    "为预处理好的张量批量。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 使用 ImageDataGenerator 从目录中读取图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2000 images belonging to 2 classes.\n",
      "Found 1000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "# 将所有的图像乘以 1/255 缩放\n",
    "train_datagen = ImageDataGenerator(rescale=1./255)\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(train_dir, target_size=(150, 150), batch_size=20, class_mode=\"binary\")\n",
    "\n",
    "validation_generator = test_datagen.flow_from_directory(validation_dir, target_size=(150, 150), batch_size=20, class_mode=\"binary\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "来看一下其中一个生成器的输出：\n",
    "\n",
    "它生成了 150×150 的 RGB 图像［形状为 (20, 150, 150, 3)］与二进制标签［形状为 (20,)］组成的批量；\n",
    "\n",
    "每个批量中包含 20 个样本（批量大小）。\n",
    "\n",
    "注意，生成器会不停地生成这些批量，它会不断循环目标文件夹中的图像。\n",
    "\n",
    "因此，我们需要在某个时刻终止（break）迭代循环。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "图像数据组成的批量: (20, 150, 150, 3)\n",
      "二进制标签组成的批量: (20,)\n"
     ]
    }
   ],
   "source": [
    "for data_batch, labels_batch in train_generator:\n",
    "    print('图像数据组成的批量:', data_batch.shape)\n",
    "    print('二进制标签组成的批量:', labels_batch.shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "利用生成器，我们让模型对数据进行拟合。\n",
    "\n",
    "我们将使用 fit_generator 方法来拟合，它在数据生成器上的效果和 fit 相同。\n",
    "\n",
    "它的第一个参数应该是一个 Python 生成器，可以不停地生成输入和目标组成的批量，比如 train_generator。\n",
    "\n",
    "因为数据是不断生成的，所以 Keras 模型要知道每一轮需要从生成器中抽取多少个样本。\n",
    "\n",
    "这是 steps_per_epoch 参数的作用：从生成器中抽取 steps_per_epoch 个批量后（即运行了 steps_per_epoch 次梯度下降），拟合过程\n",
    "将进入下一个轮次。\n",
    "\n",
    "本例中，每个批量包含 20 个样本，所以读取完所有 2000 个样本需要 100个批量。\n",
    "\n",
    "使用 fit_generator 时，你可以传入一个 validation_data 参数，其作用和在 fit 方法中类似。\n",
    "\n",
    "值得注意的是，这个参数可以是一个数据生成器，但也可以是 Numpy 数组组成的元\n",
    "组。\n",
    "\n",
    "如果向 validation_data 传入一个生成器，那么这个生成器应该能够不停地生成验证数据批量，因此你还需要指定 validation_steps 参数，说明需要从验证生成器中抽取多少个批\n",
    "次用于评估。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5-2-5 利用批量生成器拟合模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "100/100 [==============================] - 120s 1s/step - loss: 0.5911 - acc: 0.6920 - val_loss: 0.6422 - val_acc: 0.6730\n",
      "Epoch 2/30\n",
      "100/100 [==============================] - 132s 1s/step - loss: 0.5536 - acc: 0.7165 - val_loss: 0.6300 - val_acc: 0.6600\n",
      "Epoch 3/30\n",
      "100/100 [==============================] - 124s 1s/step - loss: 0.5234 - acc: 0.7285 - val_loss: 0.5725 - val_acc: 0.6780\n",
      "Epoch 4/30\n",
      "100/100 [==============================] - 106s 1s/step - loss: 0.5070 - acc: 0.7575 - val_loss: 0.5434 - val_acc: 0.6560\n",
      "Epoch 5/30\n",
      "100/100 [==============================] - 107s 1s/step - loss: 0.4779 - acc: 0.7710 - val_loss: 0.5561 - val_acc: 0.7050\n",
      "Epoch 6/30\n",
      "100/100 [==============================] - 106s 1s/step - loss: 0.4555 - acc: 0.7790 - val_loss: 0.5278 - val_acc: 0.7080\n",
      "Epoch 7/30\n",
      "100/100 [==============================] - 106s 1s/step - loss: 0.4280 - acc: 0.8040 - val_loss: 0.3730 - val_acc: 0.7190\n",
      "Epoch 8/30\n",
      "100/100 [==============================] - 106s 1s/step - loss: 0.3978 - acc: 0.8200 - val_loss: 0.3879 - val_acc: 0.7140\n",
      "Epoch 9/30\n",
      "100/100 [==============================] - 107s 1s/step - loss: 0.3704 - acc: 0.8300 - val_loss: 0.5343 - val_acc: 0.7290\n",
      "Epoch 10/30\n",
      "100/100 [==============================] - 106s 1s/step - loss: 0.3497 - acc: 0.8495 - val_loss: 0.6558 - val_acc: 0.7290\n",
      "Epoch 11/30\n",
      "100/100 [==============================] - 110s 1s/step - loss: 0.3222 - acc: 0.8630 - val_loss: 0.7069 - val_acc: 0.7130\n",
      "Epoch 12/30\n",
      "100/100 [==============================] - 116s 1s/step - loss: 0.3024 - acc: 0.8710 - val_loss: 0.3056 - val_acc: 0.7310\n",
      "Epoch 13/30\n",
      "100/100 [==============================] - 108s 1s/step - loss: 0.2820 - acc: 0.8870 - val_loss: 0.3519 - val_acc: 0.7380\n",
      "Epoch 14/30\n",
      "100/100 [==============================] - 107s 1s/step - loss: 0.2510 - acc: 0.9055 - val_loss: 0.3986 - val_acc: 0.7390\n",
      "Epoch 15/30\n",
      "100/100 [==============================] - 106s 1s/step - loss: 0.2253 - acc: 0.9135 - val_loss: 0.8490 - val_acc: 0.6950\n",
      "Epoch 16/30\n",
      "100/100 [==============================] - 105s 1s/step - loss: 0.2153 - acc: 0.9130 - val_loss: 0.8753 - val_acc: 0.7210\n",
      "Epoch 17/30\n",
      "100/100 [==============================] - 112s 1s/step - loss: 0.1924 - acc: 0.9315 - val_loss: 0.8283 - val_acc: 0.7330\n",
      "Epoch 18/30\n",
      "100/100 [==============================] - 105s 1s/step - loss: 0.1693 - acc: 0.9420 - val_loss: 0.4658 - val_acc: 0.7340\n",
      "Epoch 19/30\n",
      "100/100 [==============================] - 105s 1s/step - loss: 0.1550 - acc: 0.9430 - val_loss: 0.2179 - val_acc: 0.7460\n",
      "Epoch 20/30\n",
      "100/100 [==============================] - 105s 1s/step - loss: 0.1343 - acc: 0.9550 - val_loss: 0.6847 - val_acc: 0.7330\n",
      "Epoch 21/30\n",
      "100/100 [==============================] - 110s 1s/step - loss: 0.1239 - acc: 0.9595 - val_loss: 0.7243 - val_acc: 0.7180\n",
      "Epoch 22/30\n",
      "100/100 [==============================] - 106s 1s/step - loss: 0.1058 - acc: 0.9685 - val_loss: 1.2983 - val_acc: 0.7390\n",
      "Epoch 23/30\n",
      "100/100 [==============================] - 106s 1s/step - loss: 0.0910 - acc: 0.9725 - val_loss: 0.7853 - val_acc: 0.7060\n",
      "Epoch 24/30\n",
      "100/100 [==============================] - 107s 1s/step - loss: 0.0733 - acc: 0.9770 - val_loss: 0.2181 - val_acc: 0.7260\n",
      "Epoch 25/30\n",
      "100/100 [==============================] - 107s 1s/step - loss: 0.0658 - acc: 0.9855 - val_loss: 0.8676 - val_acc: 0.7310\n",
      "Epoch 26/30\n",
      "100/100 [==============================] - 106s 1s/step - loss: 0.0584 - acc: 0.9815 - val_loss: 0.4759 - val_acc: 0.7330\n",
      "Epoch 27/30\n",
      "100/100 [==============================] - 110s 1s/step - loss: 0.0561 - acc: 0.9860 - val_loss: 0.7324 - val_acc: 0.7060\n",
      "Epoch 28/30\n",
      "100/100 [==============================] - 113s 1s/step - loss: 0.0435 - acc: 0.9880 - val_loss: 0.5085 - val_acc: 0.7270\n",
      "Epoch 29/30\n",
      "100/100 [==============================] - 112s 1s/step - loss: 0.0348 - acc: 0.9915 - val_loss: 0.5620 - val_acc: 0.7340\n",
      "Epoch 30/30\n",
      "100/100 [==============================] - 112s 1s/step - loss: 0.0331 - acc: 0.9925 - val_loss: 1.4232 - val_acc: 0.7260\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(train_generator,steps_per_epoch=100,epochs=30,validation_data=validation_generator,validation_steps=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5-2-6 保存模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"cats_and_dogs_small_1.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来我们来分别绘制训练过程中模型在训练数据和验证数据上的损失和精度"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5-2-7 绘制训练过程中的损失曲线和精度曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Environment\\Anaconda3\\lib\\site-packages\\matplotlib\\font_manager.py:1328: UserWarning: findfont: Font family ['MicroSoft YaHei'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(len(acc))\n",
    "\n",
    "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "能看出过拟合的特征：\n",
    "\n",
    "训练精度随着时间线性增强，知道接近100%，而验证进度却停留在72%左右。\n",
    "\n",
    "因为训练样本相对较少，所以会出现**过拟合**的问题。之前我们尝试使用过几种降低过拟合的方法，例如 dropout和权重衰减（1.2正则化）\n",
    "\n",
    "现在，我们使用**数据增强**来解决过拟合的问题。\n",
    "\n",
    "**数据增强**是对于计算机视觉领域的新的方法，我们再深度模型处理图像时几乎都会用到这种方法。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5-2-8 使用数据增强"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据增强是从现有的训练样本中生成更多的训练数据，其方法是利用多种能够生成可信图像的随机变换来增强（augment）样本。\n",
    "\n",
    "在 Keras 中，这可以通过对 ImageDataGenerator 实例读取的图像执行多次随机变换来实现。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "datagen = ImageDataGenerator(\n",
    "    rotation_range=40, \n",
    "    width_shift_range=0.2,\n",
    "    height_shift_range=0.2,\n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True,\n",
    "    fill_mode=\"nearest\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "关于上面这些参数的具体含义：\n",
    "\n",
    "rotation_range: 角度值，表示图像随机旋转的角度范围\n",
    "\n",
    "width_shift和height_shift是图像在水平或者垂直方向上平移的范围（相对于总宽度或总高度的比例）\n",
    "\n",
    "shear_range是随机错切变换的角度\n",
    "\n",
    "zoom_range是图像随机缩放的范围\n",
    "\n",
    "horizontal_flip是随机将一半图像水平翻转\n",
    "\n",
    "fill_mode是用于填充新创建像素的方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5-2-9 显示几个随机增强之后的训练图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Environment\\Anaconda3\\lib\\site-packages\\matplotlib\\font_manager.py:1328: UserWarning: findfont: Font family ['MicroSoft YaHei'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 导入图像预处理工具的模块\n",
    "from keras.preprocessing import image\n",
    "import random\n",
    "\n",
    "fnames = [os.path.join(train_cats_dir, fname) for fname in os.listdir(train_cats_dir)]\n",
    "\n",
    "print(len(fnames))\n",
    "\n",
    "# 选择一张图像进行增强\n",
    "img_path = fnames[random.randint(0, 999)]\n",
    "\n",
    "# 读取图像并调整大小\n",
    "img = image.load_img(img_path, target_size=(150, 150))\n",
    "\n",
    "# 将其转换为形状 （150， 150， 3） 的 Numpy 数组\n",
    "x = image.img_to_array(img)\n",
    "\n",
    "# 将其形状改变为（1,150,150,3） 4D张量\n",
    "x = x.reshape((1,) + x.shape)\n",
    "\n",
    "# 生成随机变换后的图像批量，循环是无限的，因此需要在某个时刻终止循环\n",
    "i = 0\n",
    "for batch in datagen.flow(x, batch_size=1):\n",
    "    plt.figure(i)\n",
    "    imgplot = plt.imshow(image.array_to_img(batch[0]))\n",
    "    i += 1\n",
    "    if i % 4 == 0:\n",
    "        break\n",
    "        \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "使用数据增强来训练一个新的网络，虽然网络将不会两次看到同样的输入，但是网络看到的输入仍然是高度相关的。因为这些输入都来自于少量的原始图像，我们无法生成新的信息，而只能混合现有信息。\n",
    "\n",
    "因此，这种方法不能完全消除过拟合，为了进一步降低过拟合，我们还需要向模型中添加一个 Dropout 层，添加到密集连接分类器之前。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5-2-10 定义一个包含 dropout 的新的卷积神经网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import layers\n",
    "from keras import models\n",
    "\n",
    "model = models.Sequential()\n",
    "model.add(layers.Conv2D(32, (3, 3), activation = \"relu\", input_shape=(150, 150, 3)))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "\n",
    "model.add(layers.Conv2D(64, (3, 3), activation = \"relu\"))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "\n",
    "model.add(layers.Conv2D(128, (3, 3), activation = \"relu\"))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(128, (3, 3), activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "\n",
    "model.add(layers.Flatten())\n",
    "\n",
    "model.add(layers.Dropout(0.5))\n",
    "\n",
    "model.add(layers.Dense(512, activation = \"relu\"))\n",
    "\n",
    "model.add(layers.Dense(1, activation = \"sigmoid\"))\n",
    "\n",
    "model.compile(loss=\"binary_crossentropy\", optimizer=optimizers.RMSprop(lr=1e-4), metrics=[\"acc\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "source": [
    "# 5-2-11 使用数据增强生成器来训练卷积神经网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from tensorflow.python.client import device_lib\n",
    "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"99\"\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    print(device_lib.list_local_devices())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "train_datagen = ImageDataGenerator(rescale=1./255, rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True,)\n",
    "\n",
    "# 这里不能增强验证数据\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "        # 目标目录\n",
    "        train_dir,\n",
    "        # 将所有图像大小调整为 150 * 150\n",
    "        target_size=(150, 150),\n",
    "        batch_size=32,\n",
    "        # 因为使用了 binary_crossentropy 损失，所以需要使用二进制标签\n",
    "        class_mode='binary')\n",
    "\n",
    "validation_generator = test_datagen.flow_from_directory(\n",
    "        validation_dir,\n",
    "        target_size=(150, 150),\n",
    "        batch_size=32,\n",
    "        class_mode='binary')\n",
    "\n",
    "history = model.fit_generator(\n",
    "      train_generator,\n",
    "      steps_per_epoch=100,\n",
    "      epochs=100,\n",
    "      validation_data=validation_generator,\n",
    "      validation_steps=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5-2-12 保存模型，同时再次绘制结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "model.save('cats_and_dogs_small_2.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "结论：\n",
    "\n",
    "使用了数据增强和 dropout 之后，模型不再过拟合了，训练曲线紧紧跟随着验证曲线。\n",
    "\n",
    "现在我们的精度是85%左右，想要进一步提高精度，我们就需要使用预训练的模型。\n",
    "\n",
    "**绘制图形分析**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(len(acc))\n",
    "\n",
    "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  }
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